Effects of Decision Complexity in Goal seeking Gridworlds: A Comparison of Instance Based Learning and Reinforcement Learning Agents
Abstract
Decisions under uncertainty are often made by weighing the expected costs and benefits of the available options. The costs benefits tradeoffs may make decisions easy or difficult, particularly given uncertainty of these costs and rewards. In this research, we evaluate how a cognitive model based on Instance Based Learning Theory (IBLT) and two well-known reinforcement learning (RL) algorithms learn to make better choices in a goal-seeking gridworld task under uncertainty and on increasing degrees of decision complexity. We also use a randomagent as a base level comparison. Our results suggest that IBL and RL models are comparable in their accuracy levels on simple settings, although the RL models are more efficient than the IBL model. However, as decision complexity increases, the IBL model is not only more accurate but also more efficient than the RL models. Our results suggest that the IBL model is able to pursue highly rewarding targets even when the costs increase; while the RL models seem to get distracted by lower costs, reaching lower reward targets.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2020
- Accession Number
- AD1130203
Entities
People
- Cleotilde Gonzalez
- Thuy N. Nguyen
Organizations
- Carnegie Mellon University